CN110889599A - Order processing method and device, warehousing system, computer equipment and storage medium - Google Patents

Order processing method and device, warehousing system, computer equipment and storage medium Download PDF

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CN110889599A
CN110889599A CN201911099725.XA CN201911099725A CN110889599A CN 110889599 A CN110889599 A CN 110889599A CN 201911099725 A CN201911099725 A CN 201911099725A CN 110889599 A CN110889599 A CN 110889599A
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distributed
order
site group
site
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CN110889599B (en
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柳祎宸
李佳骏
吴航
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Beijing Wide-Sighted Robot Technology Co Ltd
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Abstract

The application relates to an order processing method and device, a warehousing system, computer equipment and a storage medium. The method comprises the following steps: acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group; obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed; and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed. By adopting the method, the overall working efficiency of the warehouse can be improved.

Description

Order processing method and device, warehousing system, computer equipment and storage medium
Technical Field
The present application relates to the field of logistics technologies, and in particular, to an order processing method and apparatus, a warehousing system, a computer device, and a computer-readable storage medium.
Background
In the warehouse operation process, the allocation scheme of orders to the sites is an important factor influencing the work efficiency of the warehouse. The quality of order distribution to the site directly affects the overall working efficiency of the warehouse, and if the distribution is not good, the efficiency of the warehouse is low.
The traditional order-to-site distribution method is mostly realized based on a greedy algorithm. However, by adopting the traditional method for distributing orders to the sites, the phenomenon that some sites have lots of tasks and some sites have no tasks often occurs, so that the working efficiency of the whole warehouse is influenced, and the working efficiency of the warehouse is lower.
Disclosure of Invention
In view of the above, it is necessary to provide an order processing method and apparatus, a warehousing system, a computer device and a computer readable storage medium for solving the technical problem that the conventional order-to-site allocation method results in low work efficiency of the warehouse.
A method of order processing, the method comprising:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
In one embodiment, the method further comprises:
obtaining the total number of tasks of the tasks allocated to all the site groups according to the number of the tasks allocated to each site group;
obtaining a score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed, wherein the score distributed to each site group by each order comprises the following steps:
obtaining the commodities and the quantity of the commodities in the bins allocated to each station group according to the commodities and the quantity of the commodities in the bins allocated to each station group and the commodities and the quantity of the commodities required by each station group;
and calculating to obtain the score of each order distributed to each site group according to the commodities and the quantity thereof remaining in the bin distributed by each site group, the commodities and the quantity thereof required by each order, the task number of the distributed tasks of each site group and the total number of the tasks of the distributed tasks of all the site groups by adopting a preset valuation function.
In one embodiment, the calculating, according to the commodities and the number thereof remaining in the bins allocated to each site group, the commodities and the number thereof required by each order, the number of tasks allocated to each site group, and the total number of tasks allocated to all the site groups, the score allocated to each site group by each order includes:
calculating to obtain a first score according to the commodities and the quantity thereof left in the distributed workbooks of each site group and the commodities and the quantity thereof required by each order;
calculating to obtain a second score according to the number of tasks of the assigned tasks of each site group and the total number of tasks of the assigned tasks of all the site groups;
and calculating the score of each order distributed to each site group according to the first score and the second score.
In one embodiment, the valuation function is:
Figure BDA0002269470500000031
where z represents the score assigned to each site group by each order,
Figure BDA0002269470500000032
to represent
Figure BDA0002269470500000033
piIndicating the number of items i, q remaining in the bin allocated to each station groupiIndicating the number of goods i required for each order, t indicating the number of kinds of goods required for each order, m indicating the number of tasks assigned to each site group, n indicating the total number of tasks assigned to all site groups, w1And w2Is a preset weight parameter.
In one embodiment, the method further comprises:
obtaining the task average number of the tasks distributed by all the site groups according to the task number of the tasks distributed by each site group;
inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed, wherein the method comprises the following steps:
and inputting the score of each order distributed to each site group, the number of tasks distributed to each site group and the average number of tasks distributed to all the site groups into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
In one embodiment, the process of constructing the minimum cost maximum network flow model includes:
in the network flow model, each order is connected with a source point, and an edge with the capacity of 1 and the cost of 0 is established;
connecting each site group with a sink, and establishing an edge with the capacity of α and the cost of 0, wherein α is the average number of tasks of the tasks distributed by all the site groups + k is the number of tasks of the tasks distributed by each site group, and k is a positive number;
connecting said each order with said each site group and establishing an edge with a capacity of 1 and a cost of β, wherein β -1 x each order is assigned a score to each site group.
An order processing apparatus, the apparatus comprising:
the task information acquisition module is used for acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities and the quantity thereof required by each site group and the task number of the tasks distributed to each site group;
the score calculation module is used for obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and the order distribution module is used for inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset maximum network flow model with minimum cost and determining the site group to which each order should be distributed.
A warehousing system, the system comprising:
a plurality of site groups;
an order processing apparatus for performing the method of any one of claims 1 to 6;
and the distribution system is used for distributing the plurality of orders to be distributed in the order pool to the corresponding station groups according to the station groups to be distributed of each order determined by the order processing equipment, so that the commodities required by each order can be selected from the bins required to be carried by the station groups.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
According to the order processing method and device, the warehousing system, the computer equipment and the computer readable storage medium, the score of each order to be distributed to each site group is calculated according to the task information distributed by the site group and the information of the order to be distributed, and the distribution scheme of the order to the site group is obtained by adopting the minimum cost and maximum network flow model. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
Drawings
FIG. 1 is a diagram of an exemplary environment in which a method for order processing may be implemented;
FIG. 2 is a flow diagram illustrating a method for order processing according to one embodiment;
FIG. 3 is a schematic flow diagram illustrating a complementary approach to obtaining a score assigned to each site group for each order in one embodiment;
FIG. 4 is a schematic flow diagram illustrating the construction of a least cost maximum network flow model in one embodiment;
FIG. 5 is a block diagram of an order processing apparatus according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The order processing method provided by the application can be applied to the application environment shown in fig. 1. The order processing device 102 is in communication connection with the order acquiring device 104, the station group 106, and the distribution system 108 is in communication connection with the order acquiring device 104 and the station group 106. Typically, site group 106 includes a plurality, and each site group includes a plurality of sites.
The order obtaining device 104 is configured to receive an order placed by a user through an input device such as a display screen or a touch screen on an application interface, generate order information, and store the order information. The order information includes the goods and the quantity thereof required by each order. The memory in site group 106 stores the assigned task information for the site group.
Specifically, the order processing device 102 first obtains order information of an order to be allocated transmitted by the order obtaining device 104 and obtains task information already allocated by a site group transmitted by the site group 106. Then, the order processing device 102 determines a site group to which each order should be allocated according to the assigned task information of the site group and the order information of the order to be assigned, and by adopting a minimum cost maximum network flow model. The overall optimal distribution scheme from the order to the station group is achieved, the carrying efficiency of the bins in the warehouse can be improved, the warehouse-out time is reduced, and therefore the overall working efficiency of the warehouse is improved.
The order processing device 102 and the distribution system 108 may be, but are not limited to, various servers (such as a local server or a cloud server), a personal computer, a notebook computer, a smart phone, a tablet computer, and a portable wearable device. The order obtaining device 104 may be a terminal device such as a personal computer, a notebook computer, a smart phone, a tablet computer, and a portable wearable device.
Optionally, the order processing method according to the embodiment of the present application may be specifically applied to a stacked three-dimensional warehousing system. The stacked three-dimensional warehousing system adopts a high-density storage mode, the warehouse is divided into a plurality of layers, each layer is provided with a plurality of tracks, and a carrying robot is arranged on the tracks. The transfer robot is used for transferring a bin below, the bin containing one or more kinds of commodities, and a plurality of bins are stacked into a pile called a stacking tower. The station is a special stacker, on which only one bin can be placed, and the required goods are manually picked from the station bin, and then the transfer robot automatically moves the station bin back to the storage area. A plurality of stacking towers and a plurality of stations jointly form a track.
In one embodiment, as shown in fig. 2, an order processing method is provided, which is described by taking the method as an example applied to the order processing device 102 in fig. 1, and includes the following steps:
s202, task information distributed to each site group in a plurality of site groups is obtained.
The task information comprises tasks distributed by each site group and the number of the tasks distributed by each site group. The assigned tasks for each station group include the assigned bins for each station group, the items required for each station group, and their quantities. The number of tasks assigned to each site group refers to the number of tasks assigned to each site group. The number of tasks allocated to each site group is the sum of the number of tasks allocated to all sites in each site group. Each site group includes a plurality of sites.
Specifically, the order processing apparatus acquires assigned task information for each of a plurality of site groups.
S204, obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in the plurality of orders to be distributed.
The goods in the bin allocated to each station group and the quantity thereof can be the types of the goods in the bin allocated to each station group and the quantity of each goods. The items and the number thereof required for each site group may be the kind of items and the number of each item required for each site group. The goods and the quantity thereof required for each order in the plurality of orders to be distributed may be the kind of goods and the quantity of each kind of goods required for each order in the plurality of orders to be distributed.
Specifically, the order processing device calculates the score assigned to each site group by each order according to the commodity type and the quantity of each commodity in the bin assigned to each site group, the commodity type and the quantity of each commodity required by each site group, and the commodity type and the quantity of each commodity required by each order in a plurality of orders to be assigned.
For example, the order processing equipment obtains the types of goods and the quantity of each goods in the bins required to be carried in the tasks of each station group according to the types of goods and the quantity of each goods in the assigned bins of each station group and the types of goods and the quantity of each goods required by each station group. Then, the order processing equipment calculates the score distributed to each site group by each order according to the type and the quantity of the commodities left in the material box required to be conveyed in the task of each site group and the type and the quantity of the commodities required by each order in a plurality of orders to be distributed.
For example, assume that there are 10 items of merchandise a, 5 items of merchandise b, and 15 items of merchandise c in the bins allocated for station group A. Station group a requires 10 items a and 3 items b. By subtracting the number of the same kind of goods, we can get: in the bin allocated by station group a, there are 0 items a, 2 items b and 15 items c remaining.
S206, inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
Alternatively, the minimum-cost maximum network flow model may be preset in the order processing apparatus.
Specifically, the order processing device inputs the score of each order assigned to each site group and the number of tasks of each site group assigned to tasks into a preset minimum cost maximum network flow model, and determines the site group to which each order should be assigned.
Optionally, after determining the station group to which each order should be assigned, a bin that is on top of the storage area and contains the items in the order is preferentially selected as the bin selected for the order.
It should be understood that after each order is assigned to a site group, the order may be further assigned to one or more sites in the site group for processing, and the embodiment of the present invention is not limited to the specific method for assigning an order to one or more sites in the site group. For example, one order may be assigned to any one or a preset number of sites in the site group, and the order may also be assigned to one or a preset number of sites with the least amount of tasks in the site group. Alternatively, if an order is assigned to multiple sites in a site group, the order may also be assigned to multiple adjacent sites.
According to the order processing method, the score of each order to be distributed to each site group is calculated according to the task information distributed by the site group and the information of the order to be distributed, and the distribution scheme of the order to the site group is obtained by adopting a minimum cost maximum network flow model. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
In one embodiment, the method further comprises the steps of:
and S212, obtaining the total number of the tasks allocated to all the site groups according to the number of the tasks allocated to each site group.
Specifically, the order processing device accumulates the number of tasks of the assigned tasks of each site group to obtain the total number of tasks of the assigned tasks of all the site groups. For example, assuming that the number of tasks allocated to the site group a is 3, the number of tasks allocated to the site group B is 4, and the number of tasks allocated to the site group C is 5, the total number of tasks allocated to the three site groups is 12 by accumulating the number of tasks allocated to the three site groups.
In one embodiment, the method involves a possible implementation process of obtaining the score assigned to each station group by each order according to the commodities and the quantity thereof in the distributed bin of each station group, the commodities and the quantity thereof required by each station group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed. On the basis of the above embodiment, S204 includes the steps of:
s2042, obtaining the commodities and the quantity thereof remaining in the bins allocated to each station group according to the commodities and the quantity thereof in the bins allocated to each station group and the commodities and the quantity thereof required by each station group;
and S2044, calculating to obtain a score distributed to each site group by each order according to the commodities and the quantity thereof remaining in the bin distributed to each site group, the commodities and the quantity thereof required by each order, the number of tasks distributed to each site group and the total number of tasks distributed to all the site groups by using a preset valuation function.
In this embodiment, the influence degree of the number of tasks allocated to each site group and the total number of tasks allocated to all site groups on the order allocation efficiency is further considered, so that the score allocated to each site group by each order is more suitable for the actual production scenario, and the accuracy of order allocation can be further improved.
Referring to fig. 3, for example, one possible implementation process of S2044 is as follows:
s204a, calculating to obtain a first score according to the commodities and the quantity thereof remaining in the distributed bin of each station group and the commodities and the quantity thereof required by each order;
s204b, calculating to obtain a second score according to the number of tasks of the assigned tasks of each site group and the total number of tasks of the assigned tasks of all the site groups;
and S204c, calculating the score of each order to each site group according to the first score and the second score.
For example, the first score
Figure BDA0002269470500000111
Second score
Figure BDA0002269470500000112
Z-z score assigned to each site group per order1+z2。
Optionally, in one embodiment, the valuation function is:
Figure BDA0002269470500000113
where z represents the score assigned to each site group by each order,
Figure BDA0002269470500000114
to represent
Figure BDA0002269470500000115
piIndicating the number of items i, q remaining in the bin allocated to each station groupiIndicating the number of goods i required for each order, t indicating the number of kinds of goods required for each order, m indicating the number of tasks assigned to each site group, n indicating the total number of tasks assigned to all site groups, w1And w2Is a preset weight parameter.
In particular, the amount of the solvent to be used,
Figure BDA0002269470500000116
may represent a similar relationship of the items required for each order to be dispensed to the items in the bins that have been dispensed for each station group. Wherein the content of the first and second substances,
Figure BDA0002269470500000117
the larger the indication, the higher the similarity of the items required for each order to be dispensed to the items in the bins that have been dispensed for each station group. The orders to be distributed can be distributed to the site groups according to the similarity of the commodity information.
Figure BDA0002269470500000118
May indicate how busy the site group is. Wherein the content of the first and second substances,
Figure BDA0002269470500000119
the larger the size, the more busy the site group is.
In the embodiment, the estimation function is adopted to adjust the influence of the order similarity relation and the busyness degree of the sites on order distribution, so that the orders to be distributed can be distributed according to the tasks distributed by the site groups, the busyness degree difference among the site groups is avoided greatly, the tasks of the site groups tend to be average as much as possible, the delivery time of the warehouse is reduced, and the working efficiency of the warehouse can be further improved.
As an implementation manner, a specific implementation process of the above embodiment is exemplarily illustrated in a data format of a binary. Where a doublet is a doublet for < goods, quantity >. On the basis of the above embodiment, the implementation process includes the following steps:
constructing a first binary group according to the commodities and the quantity thereof in the bins allocated to each station group, wherein the first binary group is represented as (a first commodity, a first quantity), the first commodity corresponds to each commodity in the bins allocated to each station group, and the first quantity is the quantity of the first commodity;
constructing a second binary group according to the commodities and the quantity thereof required by each site group, wherein the second binary group is represented as < a second commodity, a second quantity >, the second commodity corresponds to each commodity required by each site group, and the second quantity is the quantity of the second commodity;
subtracting the first binary group from the second binary group to obtain a third binary group, wherein the third binary group is expressed as < a third commodity and a third quantity >, and determining the commodities and the quantity of the commodities in the distributed bin of each station group according to the third binary group;
constructing a fourth tuple according to the commodities and the quantity thereof required by each order, wherein the fourth tuple is expressed as < fourth commodity, fourth quantity >, the fourth commodity corresponds to each commodity required by each order, and the fourth quantity is the quantity of the fourth commodity;
acquiring a preset valuation function:
Figure BDA0002269470500000121
where z represents the score each order assigns to each site group, C represents the third tuple, D represents the fourth tuple,
Figure BDA0002269470500000122
to represent
Figure BDA0002269470500000123
The binary division yields [0, 1 ]]M represents the number of tasks of the tasks allocated to each site group, n represents the total number of the tasks allocated to all the site groups, and w1 and w2 are preset weight parameters;
and inputting the obtained third binary group, the obtained fourth binary group, the task number of the tasks distributed to each site group and the total number of the tasks distributed to all the site groups into a valuation function to obtain the score distributed to each site group by each order.
In one embodiment, please refer to fig. 4, which relates to a process of constructing a minimum cost maximum network flow model. On the basis of the above embodiment, the construction process includes the following steps:
s222, in the network flow model, each order is connected with a source point, and an edge with the capacity of 1 and the cost of 0 is established.
And S224, connecting each site group with a sink, and establishing an edge with the capacity of α and the cost of 0, wherein α is the average number of tasks of the assigned tasks of all the site groups + k-the number of tasks of the assigned tasks of each site group, and k is a positive number, and optionally k takes any value from 4 to 10.
S226, connect each order with each site group and establish an edge with capacity of 1 and cost of β, where β ═ 1 × each order assigns a score to each site group.
The above-mentioned minimum cost maximum network flow model is one of the network flow models. For the network flow model, water flow can be analogized, which is equivalent to a pipeline diagram, each water pipe has a certain volume, the water flow in the water pipe cannot exceed the volume, a source point is equivalent to a place for discharging water, water can be continuously discharged, a sink point is capable of receiving infinite water, but the water flow is limited by the volume of the pipeline, and the maximum flow is obtained by calculating the maximum amount of water which can flow from the source point to the sink point in the diagram. If a certain fee is charged for every 1 unit of water flowing in the pipeline, the minimum fee maximum network flow represents: the minimum cost is what the maximum flow is, i.e. the maximum water flow in the pipe. The minimum cost maximum network flow is defined as: the least cost maximum flows are based on the maximum flow and network flow problems. The weighted directed graph G (V, E) is a special capacity network, all edges (u, V) E contain c (u, V) E R +, which is called the capacity of the arc, and w (u, V) E R + is called the cost of the edge. The total cost of a feasible flow in a capacity network is sigma (f (u, v) × w (u, v), and the lowest total cost of all the maximum flows is called the minimum cost maximum network flow of the capacity network.
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided an order processing apparatus 30, wherein the order processing apparatus 30 includes:
a task information obtaining module 302, configured to obtain task information allocated to each station group in a plurality of station groups, where the task information includes a bin allocated to each station group, commodities and the quantity thereof required by each station group, and the number of tasks allocated to each station group;
a score calculating module 304, configured to obtain a score assigned to each site group by each order according to the commodities and the number thereof in the bin assigned to each site group, the commodities and the number thereof required by each site group, and the commodities and the number thereof required by each order in the multiple orders to be assigned;
the order distribution module 306 is configured to input the score of each order distributed to each site group and the task number of the tasks distributed to each site group into a preset minimum cost maximum network flow model, and determine the site group to which each order should be distributed.
The order processing device calculates the score of each order to be distributed to each site group according to the task information distributed by the site group and the information of the order to be distributed, and obtains the distribution scheme of the order to the site group by adopting a minimum cost maximum network flow model. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
For specific limitations of the order processing apparatus, reference may be made to the above limitations of the order processing method, which are not described herein again. The modules in the order processing device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, there is provided a warehousing system comprising:
a plurality of site groups;
an order processing apparatus for performing the method of any one of the above embodiments;
and the distribution system is used for distributing a plurality of orders to be distributed in the order pool to the corresponding station groups according to the station groups to be distributed of each order determined by the order processing equipment, so that the commodities required by each order can be selected from the bins required to be carried by the station groups.
The warehousing system obtains the distribution scheme of the orders to the site groups based on the minimum-cost maximum network flow model adopted by the order processing equipment. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data generated in the order processing process. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an order processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
The computer device calculates the score of each order to be distributed to each site group according to the task information distributed by the site group and the information of the order to be distributed, and obtains the distribution scheme of the order to the site group by adopting a minimum cost maximum network flow model. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
The computer-readable storage medium calculates the score of each order to be distributed to each site group according to the task information distributed by the site group and the information of the orders to be distributed, and obtains the distribution scheme of the orders to the site group by adopting a minimum cost maximum network flow model. The distribution relation of the orders to the station groups can be considered globally by the model, so that the distribution scheme of the orders to the station groups can achieve global optimization, the condition that some stations have too many tasks and some stations have no tasks is avoided, the carrying efficiency of the bins in the warehouse is improved, the warehouse-out time is reduced, and the overall working efficiency of the warehouse is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application.

Claims (10)

1. An order processing method, characterized in that the method comprises:
acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities required by each site group and the quantity of the commodities and the quantity of tasks distributed to each site group;
obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
2. The method of claim 1, further comprising:
obtaining the total number of tasks of the tasks allocated to all the site groups according to the number of the tasks allocated to each site group;
obtaining a score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed, wherein the score distributed to each site group by each order comprises the following steps:
obtaining the commodities and the quantity of the commodities in the bins allocated to each station group according to the commodities and the quantity of the commodities in the bins allocated to each station group and the commodities and the quantity of the commodities required by each station group;
and calculating to obtain the score of each order distributed to each site group according to the commodities and the quantity thereof remaining in the bin distributed by each site group, the commodities and the quantity thereof required by each order, the task number of the distributed tasks of each site group and the total number of the tasks of the distributed tasks of all the site groups by adopting a preset valuation function.
3. The method of claim 2, wherein said calculating a score assigned to each site group by each order based on the remaining items in the assigned bins of each site group and their quantity, the items required for each order and their quantity, the number of tasks assigned to each site group and the total number of tasks assigned to all the site groups comprises:
calculating to obtain a first score according to the commodities and the quantity thereof left in the distributed workbooks of each site group and the commodities and the quantity thereof required by each order;
calculating to obtain a second score according to the number of tasks of the assigned tasks of each site group and the total number of tasks of the assigned tasks of all the site groups;
and calculating the score of each order distributed to each site group according to the first score and the second score.
4. The method of claim 2, wherein the valuation function is:
Figure FDA0002269470490000021
where z represents the score assigned to each site group by each order,
Figure FDA0002269470490000022
represents sigmai∈Order to be allocated
Figure FDA0002269470490000023
piIndicating the number of items i, q remaining in the bin allocated to each station groupiIndicates the number of items i required for each order, and t indicates the number of items required for each orderThe number of kinds of commodities, m represents the number of tasks assigned to each site group, n represents the total number of tasks assigned to all site groups, w1And w2Is a preset weight parameter.
5. The method of claim 1, further comprising:
obtaining the task average number of the tasks distributed by all the site groups according to the task number of the tasks distributed by each site group;
inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed, wherein the method comprises the following steps:
and inputting the score of each order distributed to each site group, the number of tasks distributed to each site group and the average number of tasks distributed to all the site groups into a preset minimum cost maximum network flow model, and determining the site group to which each order should be distributed.
6. The method of claim 5, wherein the constructing of the least cost maximum network flow model comprises:
in the network flow model, each order is connected with a source point, and an edge with the capacity of 1 and the cost of 0 is established;
connecting each site group with a sink, and establishing an edge with the capacity of α and the cost of 0, wherein α is the average number of tasks of the tasks distributed by all the site groups + k is the number of tasks of the tasks distributed by each site group, and k is a positive number;
connecting said each order with said each site group and establishing an edge with a capacity of 1 and a cost of β, wherein β -1 x each order is assigned a score to each site group.
7. An order processing apparatus, characterized in that the apparatus comprises:
the task information acquisition module is used for acquiring task information distributed to each site group in a plurality of site groups, wherein the task information comprises a bin distributed to each site group, commodities and the quantity thereof required by each site group and the task number of the tasks distributed to each site group;
the score calculation module is used for obtaining the score distributed to each site group by each order according to the commodities and the quantity thereof in the distributed bin of each site group, the commodities and the quantity thereof required by each site group, and the commodities and the quantity thereof required by each order in a plurality of orders to be distributed;
and the order distribution module is used for inputting the score of each order distributed to each site group and the task number of the distributed tasks of each site group into a preset maximum network flow model with minimum cost and determining the site group to which each order should be distributed.
8. A warehousing system, characterized in that the system comprises:
a plurality of site groups;
an order processing apparatus for performing the method of any one of claims 1 to 6;
and the distribution system is used for distributing the orders to be distributed in the order pool to the corresponding station groups according to the station groups to be distributed of the orders determined by the order processing equipment, so that the commodities required by the orders can be selected from the bins distributed by the station groups.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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